Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns. An active learner may pose queries, usually in the form of unlabeled data instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant or easily obtained, but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for successful active learning, a summary of problem setting variants and practical issues, and a discussion of related topics in machine learning research are also presented.

Active Learning Literature Survey

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13A— actual speedup over AlexNet while maintaining comparable accuracy.

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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data.

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Dual Attention Network for Scene Segmentation

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification. On one hand, labeled text data is more widely available than the labeled images for classification tasks. On the other hand, text data tends to have natural semantic interpretability, and they are often more directly related to class labels. On the contrary, the image features are not directly related to concepts inherent in class labels. One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images. This is implemented by learning a transfer function as a bridge to propagate the labels between two multimodal spaces. However, the intermodal label transfers could be undermined by blindly transferring the labels of noisy texts to annotate images. To mitigate this problem, we present an intramodal label transfer process, which complements the intermodal label transfer by transferring the image labels instead when relevant text is absent from the source corpus. In addition, we generalize the inter-modal label transfer to zero-shot learning scenario where there are only text examples available to label unseen classes of images without any positive image examples. We evaluate our algorithm on an image classification task and show the effectiveness with respect to the other compared algorithms.

Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes

3D object representation learning is a fundamental challenge in computer vision to draw inferences about the 3D world. Recent advances in deep learning have shown their efficiency in 3D object recognition, among which view-based methods have performed best so far. However, feature learning of multiple views in existing methods is mostly trained in a supervised fashion, which often requires a large amount of data labels with high cost. Hence, it is critical to learn multi-view feature representations in a self-supervised fashion. To this end, we propose a novel self-supervised learning paradigm of Multi-View Transformation Equivariant Representations (MV-TER), exploiting the equivariant transformations of a 3D object and its projected multiple views. Specifically, we perform a 3D transformation on a 3D object, and obtain multiple views before and after transformation via projection. Then, we self-train a representation learning module to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after transformation. Experimental results demonstrate that the proposed MV-TER significantly outperforms the state-of-the-art view-based approaches in 3D object classification and retrieval tasks.

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Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations

The discovery of truth is a critical step toward effective information and knowledge utilization, especially in Web services, social media networks, and sensor networks. Typically, a set of sources with varying reliability claim observations about a set of objects and the goal is to jointly discover the true fact for each object and the trustworthy degree of each source. In this paper, we propose a latent Dirichlet truth (LDT) discovery model to approach this problem. It defines a random field over all the possible configurations of the trustworthy degrees of sources and facts, and the most probable configuration is inferred by a maximum a posteriori criterion over the observed claims. We note that a typical source is usually made of mixed trustworthy and untrustworthy components, since it can make true or false claims on different objects. While most of the existing algorithms do not attempt separate the untrustworthy component from the trustworthy one in each source, the proposed model explicitly identifies untrustworthy component in each source. This makes the LDT model more capable of separating the trustworthy and untrustworthy components, and in turn improves the accuracy of truth discovery. Experiments on real data sets show competitive results compared with existing algorithms.

Latent Dirichlet Truth Discovery: Separating Trustworthy and Untrustworthy Components in Data Sources

Hashing has been widely used for approximate nearest neighbor search of high-dimensional multimedia data. In this paper, we propose a novel hash learning framework that maps high-dimensional multimodal data into a common Hamming space where the cross-modal similarity can be measured using Hamming distance. Unlike existing cross-modal hashing methods that learn hash functions in the form of numeric quantization of linear projections, the proposed hash learning algorithm encodes features' ranking properties and takes advantage of rank correlations which are known to be scale-invariant, numerically stable and highly nonlinear. Specifically, we learn two groups of subspaces jointly, one for each modality, so that the ranking orders in those subspaces maximally preserve the cross-modal similarity. Extensive experiments on realworld datasets demonstrate superiority of the proposed methods compared to state-of-the-arts.

Cross-modal hashing through ranking subspace learning

In cardiac interventions, localization of guiding catheter tip in 2D fluoroscopic images is important to specify ves-sel branches and calibrate vessels with stenosis. While detection of guiding catheter tip is not trivial in contrast-free images due to low dose radiation as well as occlusion by other devices, it is even more challenging in contrast-filled images. As contrast-filled vessels become visible in X-ray imaging, the landmark of guiding catheter tip can often be completely occluded by the contrast medium. It is difficult even for human eyes to precisely localize the catheter tip from a single angiography image. Physicians have to rely on information before the inject of contrast medium to localize the guiding catheter tip occluded by contrast medium. Automatic landmark detection when occlusion happens is important and can significantly simplify the intervention workflow. To address this problem, we propose a novel Cascade Attention Machine (CAM) model. It borrows the idea of how human experts localize the catheter tip by first per-forming landmark detection when occlusion does not hap-pen, then leveraging this information as prior knowledge to assist the occluded detection. Attention maps are computed from non-occluded detection to further refine the heatmaps for occluded detection to guide the inference focusing on related regions. Experiments on X-ray angiography demonstrate the promising performance compared with the state-of-the-art baselines. It shows that the CAM can capture the relation between situations with and without occlusion to achieve precise detection of occluded landmark.

Guo-Jun Qi

Papers

Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes

Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations

Latent Dirichlet Truth Discovery: Separating Trustworthy and Untrustworthy Components in Data Sources

Cross-modal hashing through ranking subspace learning

Cascade Attention Machine for Occluded Landmark Detection in 2D X-Ray Angiography